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Authors: Eva Coll (1), Elena Martinez-Garcia (1), Antoine Lesur (2), Silvia Cabrera (3), Xavier Matias-Guiu (4), Marc Hirschfeld (5,6), Jasmin Asberger (5), María de los Ángeles Casares de Cal (7), Antonio Gómez-Tato (7), Bruno Domon (2), Antonio Gil-Moreno (1,3), Eva Colas (1)
Endometrial cancer (EC) diagnosis relies on the observation of tumor cells in uterine aspirates, but it is associated with significant rates of undiagnosed patients and incorrectly diagnosed patients. We aimed to identify biomarker signatures in the fluid sample to overcome these limitations. The levels of 52 proteins were measured from two independent cohorts of patients of 38 and 116 patients (controls and EC including endometrioid and serous EC subtypes) by LC-PRM. Great values of sensitivity and specificity were achieved by a 2-panel signature for detecting EC cases and a 3-panel signature for the discrimination of EC subtypes. This study will improve diagnosis and assist in the prediction of the optimal surgical treatment.
Endometrial cancer (EC) diagnosis relies on the observation of tumor cells in endometrial biopsies obtained by aspiration (i.e., uterine aspirates), but it is associated with 22% undiagnosed patients and up to 50% of incorrectly assigned EC histotype and grade. We aimed to identify biomarker signatures in the fluid uterine aspirates to overcome these limitations.
The levels of 52 proteins were measured in the fluid fraction of uterine aspirates from two independent cohorts of patients of 38 and 116 patients by LC-PRM, the latest generation of targeted mass-spectrometry acquisition. A logistic regression model was used to assess the power of protein panels to differentiate between EC and non-EC patients and between EC histological subtypes. The robustness of the panels was assessed by the "leave-one-out" cross-validation procedure performed in the cohort of 116 patients and 38 patients.
The levels of 28 proteins were significantly higher in EC patients (n=69) compared to controls (n=47). The combination of MMP9 and KPYM exhibited 94% sensitivity and 87% specificity for detecting EC cases. This panel perfectly complemented the standard diagnosis, achieving 100% of correct diagnosis in this dataset. Nine proteins were significantly increased in endometrioid EC (n=49) compared to serous EC (n=20). The combination of CTNB1, XPO2 and CAPG achieved 95% sensitivity and 96% specificity for the discrimination of these subtypes.
Conclusions & Discussion
We developed uterine aspirate-based signatures to diagnose EC and classify tumors in the most prevalent histological subtypes. This will improve diagnosis and assist in the prediction of the optimal surgical treatment.
References & Acknowledgements:
IP Royalty: no
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